Overview

Dataset statistics

Number of variables60
Number of observations15120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory480.0 B

Variable types

Numeric10
Categorical50

Alerts

Climatic_Zone_3 is highly overall correlated with Geologic_Zone_5 and 1 other fieldsHigh correlation
Climatic_Zone_4 is highly overall correlated with Soil_Type_10High correlation
Climatic_Zone_5 is highly overall correlated with Soil_Type_14High correlation
Climatic_Zone_6 is highly overall correlated with Geologic_Zone_7 and 1 other fieldsHigh correlation
Climatic_Zone_8 is highly overall correlated with Elevation and 2 other fieldsHigh correlation
Elevation is highly overall correlated with Climatic_Zone_8 and 5 other fieldsHigh correlation
Geologic_Zone_2 is highly overall correlated with Geologic_Zone_7 and 2 other fieldsHigh correlation
Geologic_Zone_5 is highly overall correlated with Climatic_Zone_3 and 1 other fieldsHigh correlation
Geologic_Zone_7 is highly overall correlated with Climatic_Zone_6 and 3 other fieldsHigh correlation
Hillshade_3pm is highly overall correlated with Hillshade_9am and 1 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pm and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points_log is highly overall correlated with ElevationHigh correlation
Horizontal_Distance_To_Roadways_log is highly overall correlated with ElevationHigh correlation
Slope is highly overall correlated with Hillshade_NoonHigh correlation
Soil_Type_10 is highly overall correlated with Climatic_Zone_4High correlation
Soil_Type_14 is highly overall correlated with Climatic_Zone_5High correlation
Soil_Type_17 is highly overall correlated with Climatic_Zone_6 and 1 other fieldsHigh correlation
Soil_Type_22 is highly overall correlated with Geologic_Zone_2High correlation
Soil_Type_23 is highly overall correlated with Geologic_Zone_2 and 1 other fieldsHigh correlation
Soil_Type_38 is highly overall correlated with Climatic_Zone_8High correlation
Soil_Type_39 is highly overall correlated with Climatic_Zone_8High correlation
Soil_Type_40 is highly overall correlated with ElevationHigh correlation
Soil_Type_8 is highly overall correlated with Climatic_Zone_3 and 1 other fieldsHigh correlation
Wilderness_Area_3 is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Wilderness_Area_4 is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Wilderness_Area_2 is highly imbalanced (76.9%)Imbalance
Climatic_Zone_3 is highly imbalanced (99.7%)Imbalance
Climatic_Zone_5 is highly imbalanced (91.0%)Imbalance
Climatic_Zone_6 is highly imbalanced (70.4%)Imbalance
Geologic_Zone_2 is highly imbalanced (62.9%)Imbalance
Geologic_Zone_5 is highly imbalanced (99.7%)Imbalance
Soil_Type_2 is highly imbalanced (75.1%)Imbalance
Soil_Type_3 is highly imbalanced (64.7%)Imbalance
Soil_Type_4 is highly imbalanced (69.1%)Imbalance
Soil_Type_5 is highly imbalanced (90.6%)Imbalance
Soil_Type_6 is highly imbalanced (73.6%)Imbalance
Soil_Type_7 is highly imbalanced (99.9%)Imbalance
Soil_Type_8 is highly imbalanced (99.8%)Imbalance
Soil_Type_9 is highly imbalanced (99.6%)Imbalance
Soil_Type_11 is highly imbalanced (83.2%)Imbalance
Soil_Type_12 is highly imbalanced (87.5%)Imbalance
Soil_Type_13 is highly imbalanced (78.6%)Imbalance
Soil_Type_14 is highly imbalanced (91.0%)Imbalance
Soil_Type_16 is highly imbalanced (94.0%)Imbalance
Soil_Type_17 is highly imbalanced (74.7%)Imbalance
Soil_Type_18 is highly imbalanced (97.1%)Imbalance
Soil_Type_19 is highly imbalanced (96.6%)Imbalance
Soil_Type_20 is highly imbalanced (92.8%)Imbalance
Soil_Type_21 is highly imbalanced (99.2%)Imbalance
Soil_Type_22 is highly imbalanced (84.8%)Imbalance
Soil_Type_23 is highly imbalanced (71.8%)Imbalance
Soil_Type_24 is highly imbalanced (87.3%)Imbalance
Soil_Type_25 is highly imbalanced (99.5%)Imbalance
Soil_Type_26 is highly imbalanced (96.9%)Imbalance
Soil_Type_27 is highly imbalanced (99.3%)Imbalance
Soil_Type_28 is highly imbalanced (99.4%)Imbalance
Soil_Type_29 is highly imbalanced (57.5%)Imbalance
Soil_Type_30 is highly imbalanced (71.9%)Imbalance
Soil_Type_31 is highly imbalanced (85.8%)Imbalance
Soil_Type_32 is highly imbalanced (74.0%)Imbalance
Soil_Type_33 is highly imbalanced (75.3%)Imbalance
Soil_Type_34 is highly imbalanced (98.7%)Imbalance
Soil_Type_35 is highly imbalanced (94.1%)Imbalance
Soil_Type_36 is highly imbalanced (98.9%)Imbalance
Soil_Type_37 is highly imbalanced (97.8%)Imbalance
Soil_Type_38 is highly imbalanced (71.7%)Imbalance
Soil_Type_39 is highly imbalanced (74.9%)Imbalance
Soil_Type_40 is highly imbalanced (80.5%)Imbalance

Reproduction

Analysis started2024-02-20 22:32:00.822184
Analysis finished2024-02-20 22:32:26.959107
Duration26.14 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

HIGH CORRELATION 

Distinct1676
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.2556912 × 10-16
Minimum-2.0803313
Maximum2.6285472
Zeros0
Zeros (%)0.0%
Negative7489
Negative (%)49.5%
Memory size118.2 KiB
2024-02-20T23:32:27.131562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.0803313
5-th percentile-1.5123064
Q1-0.89654835
median0.012768784
Q30.86003278
95-th percentile1.5473906
Maximum2.6285472
Range4.7088785
Interquartile range (IQR)1.7565811

Descriptive statistics

Standard deviation1.0000331
Coefficient of variation (CV)-4.4333775 × 1015
Kurtosis-1.0884902
Mean-2.2556912 × 10-16
Median Absolute Deviation (MAD)0.88306389
Skewness0.074424175
Sum-3.2969183 × 10-12
Variance1.0000661
MonotonicityNot monotonic
2024-02-20T23:32:27.293603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1726749475 27
 
0.2%
0.1153951279 23
 
0.2%
0.2418880629 22
 
0.1%
-1.003948015 22
 
0.1%
1.48772414 22
 
0.1%
-0.9944013779 22
 
0.1%
-1.011107992 21
 
0.1%
1.537843983 21
 
0.1%
0.5330604795 21
 
0.1%
-0.839268533 21
 
0.1%
Other values (1666) 14898
98.5%
ValueCountFrequency (%)
-2.080331292 1
 
< 0.1%
-2.051691382 1
 
< 0.1%
-2.049304723 1
 
< 0.1%
-2.034984768 3
< 0.1%
-2.027824791 1
 
< 0.1%
-2.011118177 2
< 0.1%
-1.999184881 3
< 0.1%
-1.994411563 1
 
< 0.1%
-1.992024904 2
< 0.1%
-1.989638244 1
 
< 0.1%
ValueCountFrequency (%)
2.628547215 1
< 0.1%
2.626160556 1
< 0.1%
2.623773897 1
< 0.1%
2.616613919 2
< 0.1%
2.604680624 1
< 0.1%
2.595133987 1
< 0.1%
2.592747328 1
< 0.1%
2.578427373 2
< 0.1%
2.5593341 1
< 0.1%
2.554560781 2
< 0.1%

Slope
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8559522 × 10-16
Minimum-1.9400195
Maximum3.9186784
Zeros0
Zeros (%)0.0%
Negative8284
Negative (%)54.8%
Memory size118.2 KiB
2024-02-20T23:32:27.439054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9400195
5-th percentile-1.3600084
Q1-0.76827988
median-0.18241009
Q30.63780761
95-th percentile1.8095472
Maximum3.9186784
Range5.8586979
Interquartile range (IQR)1.4060875

Descriptive statistics

Standard deviation1.0000331
Coefficient of variation (CV)5.388248 × 1015
Kurtosis-0.25286265
Mean1.8559522 × 10-16
Median Absolute Deviation (MAD)0.70304375
Skewness0.53256718
Sum2.9558578 × 10-12
Variance1.0000661
MonotonicityNot monotonic
2024-02-20T23:32:27.605408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.651105924 748
 
4.9%
-0.5339319664 718
 
4.7%
-0.4167580088 708
 
4.7%
-0.7682798817 703
 
4.6%
-0.8854538393 688
 
4.6%
-0.1824100936 672
 
4.4%
-0.2995840512 636
 
4.2%
-0.06523613593 626
 
4.1%
-1.002627797 614
 
4.1%
0.05193782169 586
 
3.9%
Other values (41) 8421
55.7%
ValueCountFrequency (%)
-1.940019458 11
 
0.1%
-1.8228455 55
 
0.4%
-1.705671543 134
 
0.9%
-1.588497585 235
 
1.6%
-1.471323627 321
2.1%
-1.35414967 395
2.6%
-1.236975712 460
3.0%
-1.119801755 560
3.7%
-1.002627797 614
4.1%
-0.8854538393 688
4.6%
ValueCountFrequency (%)
3.918678423 1
 
< 0.1%
3.801504466 5
 
< 0.1%
3.684330508 2
 
< 0.1%
3.56715655 2
 
< 0.1%
3.449982593 7
< 0.1%
3.332808635 7
< 0.1%
3.215634678 5
 
< 0.1%
3.09846072 11
0.1%
2.981286762 8
0.1%
2.864112805 16
0.1%

Hillshade_9am
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79717246
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-20T23:32:27.775852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.49480198
Q10.71782178
median0.83168317
Q30.91089109
95-th percentile0.98514851
Maximum1
Range1
Interquartile range (IQR)0.19306931

Descriptive statistics

Standard deviation0.15167528
Coefficient of variation (CV)0.19026658
Kurtosis1.0729703
Mean0.79717246
Median Absolute Deviation (MAD)0.094059406
Skewness-1.0754914
Sum12053.248
Variance0.02300539
MonotonicityNot monotonic
2024-02-20T23:32:27.942710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8712871287 285
 
1.9%
0.8613861386 280
 
1.9%
0.8811881188 264
 
1.7%
0.8514851485 257
 
1.7%
0.8663366337 252
 
1.7%
0.9108910891 242
 
1.6%
0.900990099 234
 
1.5%
0.8762376238 233
 
1.5%
0.896039604 231
 
1.5%
0.8910891089 231
 
1.5%
Other values (166) 12611
83.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.0198019802 1
< 0.1%
0.07920792079 1
< 0.1%
0.08415841584 1
< 0.1%
0.09405940594 1
< 0.1%
0.09900990099 2
< 0.1%
0.1138613861 1
< 0.1%
0.1188118812 1
< 0.1%
0.1237623762 1
< 0.1%
0.1435643564 2
< 0.1%
ValueCountFrequency (%)
1 185
1.2%
0.995049505 208
1.4%
0.9900990099 203
1.3%
0.9851485149 209
1.4%
0.9801980198 213
1.4%
0.9752475248 224
1.5%
0.9702970297 204
1.3%
0.9653465347 200
1.3%
0.9603960396 181
1.2%
0.9554455446 188
1.2%

Hillshade_Noon
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77332736
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-20T23:32:28.337820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.48387097
Q10.69677419
median0.8
Q30.87741935
95-th percentile0.97419355
Maximum1
Range1
Interquartile range (IQR)0.18064516

Descriptive statistics

Standard deviation0.14707928
Coefficient of variation (CV)0.19019019
Kurtosis1.0263935
Mean0.77332736
Median Absolute Deviation (MAD)0.090322581
Skewness-0.94274704
Sum11692.71
Variance0.021632314
MonotonicityNot monotonic
2024-02-20T23:32:28.515920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8129032258 331
 
2.2%
0.8322580645 331
 
2.2%
0.8193548387 322
 
2.1%
0.8064516129 309
 
2.0%
0.8580645161 303
 
2.0%
0.8516129032 301
 
2.0%
0.7677419355 298
 
2.0%
0.8387096774 295
 
2.0%
0.8 294
 
1.9%
0.7870967742 289
 
1.9%
Other values (130) 12047
79.7%
ValueCountFrequency (%)
0 2
< 0.1%
0.01935483871 1
 
< 0.1%
0.05161290323 2
< 0.1%
0.07741935484 2
< 0.1%
0.08387096774 1
 
< 0.1%
0.1032258065 1
 
< 0.1%
0.1225806452 1
 
< 0.1%
0.1290322581 1
 
< 0.1%
0.135483871 1
 
< 0.1%
0.1419354839 3
< 0.1%
ValueCountFrequency (%)
1 134
0.9%
0.9935483871 143
0.9%
0.9870967742 157
1.0%
0.9806451613 162
1.1%
0.9741935484 163
1.1%
0.9677419355 178
1.2%
0.9612903226 189
1.2%
0.9548387097 195
1.3%
0.9483870968 212
1.4%
0.9419354839 210
1.4%

Hillshade_3pm
Real number (ℝ)

HIGH CORRELATION 

Distinct248
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5357654
Minimum0
Maximum1
Zeros95
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-20T23:32:28.701460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.20318725
Q10.42231076
median0.5498008
Q30.66135458
95-th percentile0.8247012
Maximum1
Range1
Interquartile range (IQR)0.23904382

Descriptive statistics

Standard deviation0.18354603
Coefficient of variation (CV)0.34258657
Kurtosis-0.060996158
Mean0.5357654
Median Absolute Deviation (MAD)0.11952191
Skewness-0.35341849
Sum8100.7729
Variance0.033689146
MonotonicityNot monotonic
2024-02-20T23:32:28.859063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.577689243 178
 
1.2%
0.5697211155 166
 
1.1%
0.5816733068 165
 
1.1%
0.593625498 162
 
1.1%
0.5657370518 161
 
1.1%
0.5537848606 159
 
1.1%
0.5258964143 151
 
1.0%
0.5378486056 151
 
1.0%
0.5896414343 148
 
1.0%
0.5099601594 148
 
1.0%
Other values (238) 13531
89.5%
ValueCountFrequency (%)
0 95
0.6%
0.00796812749 1
 
< 0.1%
0.01195219124 4
 
< 0.1%
0.01593625498 1
 
< 0.1%
0.01992031873 3
 
< 0.1%
0.02390438247 3
 
< 0.1%
0.03187250996 2
 
< 0.1%
0.03585657371 4
 
< 0.1%
0.03984063745 5
 
< 0.1%
0.0438247012 3
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9920318725 1
 
< 0.1%
0.9880478088 1
 
< 0.1%
0.9800796813 1
 
< 0.1%
0.9760956175 2
 
< 0.1%
0.9721115538 6
< 0.1%
0.96812749 3
< 0.1%
0.9641434263 3
< 0.1%
0.9601593625 4
< 0.1%
0.9561752988 5
< 0.1%

Aspect_sin
Real number (ℝ)

Distinct361
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0046813285
Minimum-0.99999034
Maximum0.99991226
Zeros101
Zeros (%)0.7%
Negative7583
Negative (%)50.2%
Memory size118.2 KiB
2024-02-20T23:32:29.028529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.99999034
5-th percentile-0.98803162
Q1-0.7271425
median-3.0144353 × 10-5
Q30.69608013
95-th percentile0.98935825
Maximum0.99991226
Range1.9999026
Interquartile range (IQR)1.4232226

Descriptive statistics

Standard deviation0.70413682
Coefficient of variation (CV)-150.41389
Kurtosis-1.4910616
Mean-0.0046813285
Median Absolute Deviation (MAD)0.70871055
Skewness0.0013780137
Sum-70.781687
Variance0.49580866
MonotonicityNot monotonic
2024-02-20T23:32:29.274502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8509035245 128
 
0.8%
0.8939966636 114
 
0.8%
0 101
 
0.7%
0.0883686861 89
 
0.6%
0.2538233628 84
 
0.6%
0.9268185054 83
 
0.5%
0.1673557003 82
 
0.5%
-0.7509872468 82
 
0.5%
-0.3216224032 79
 
0.5%
0.5661076369 78
 
0.5%
Other values (351) 14200
93.9%
ValueCountFrequency (%)
-0.9999903395 52
0.3%
-0.9999902066 67
0.4%
-0.9997558399 29
0.2%
-0.9997551734 70
0.5%
-0.9992080341 15
 
0.1%
-0.9992068342 66
0.4%
-0.9983470938 31
0.2%
-0.9983453609 41
0.3%
-0.9971732888 28
 
0.2%
-0.9971710234 19
 
0.1%
ValueCountFrequency (%)
0.9999122599 36
0.2%
0.9999118601 49
0.3%
0.9995210918 24
 
0.2%
0.9995201586 51
0.3%
0.9988166912 17
 
0.1%
0.9988152247 72
0.5%
0.9977992787 31
0.2%
0.9977972794 22
 
0.1%
0.9964691731 52
0.3%
0.9964666418 19
 
0.1%

Aspect_cos
Real number (ℝ)

Distinct361
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015508745
Minimum-1
Maximum1
Zeros0
Zeros (%)0.0%
Negative7358
Negative (%)48.7%
Memory size118.2 KiB
2024-02-20T23:32:29.441769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.98870914
Q1-0.69289582
median0.03982088
Q30.7179641
95-th percentile0.98999675
Maximum1
Range2
Interquartile range (IQR)1.4108599

Descriptive statistics

Standard deviation0.70992607
Coefficient of variation (CV)45.775855
Kurtosis-1.5058856
Mean0.015508745
Median Absolute Deviation (MAD)0.70233332
Skewness-0.038413496
Sum234.49223
Variance0.50399502
MonotonicityNot monotonic
2024-02-20T23:32:29.601337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5253219888 128
 
0.8%
-0.4480736161 114
 
0.8%
1 101
 
0.7%
-0.9960878351 89
 
0.6%
-0.9672505883 84
 
0.6%
0.3755095978 83
 
0.5%
0.9858965816 82
 
0.5%
0.6603167082 82
 
0.5%
-0.9468680108 79
 
0.5%
0.8243313311 78
 
0.5%
Other values (351) 14200
93.9%
ValueCountFrequency (%)
-0.9999999995 63
0.4%
-0.9999608264 59
0.4%
-0.9998438418 42
0.3%
-0.999647456 52
0.3%
-0.9993743503 22
 
0.1%
-0.9990208133 69
0.5%
-0.9985916722 22
 
0.1%
-0.9980810948 25
 
0.2%
-0.9974960527 26
 
0.2%
-0.996828595 29
0.2%
ValueCountFrequency (%)
1 101
0.7%
0.9999610928 74
0.5%
0.9998433086 47
0.3%
0.9996482559 17
 
0.1%
0.9993732837 66
0.4%
0.9990221465 13
 
0.1%
0.9985900724 53
0.4%
0.9980829609 14
 
0.1%
0.9974939203 31
 
0.2%
0.9968309934 41
0.3%

Horizontal_Distance_To_Roadways_log
Real number (ℝ)

HIGH CORRELATION 

Distinct3274
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0075883 × 10-16
Minimum-8.0461852
Maximum1.9214062
Zeros0
Zeros (%)0.0%
Negative7045
Negative (%)46.6%
Memory size118.2 KiB
2024-02-20T23:32:29.798358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-8.0461852
5-th percentile-1.8421125
Q1-0.55277767
median0.065836743
Q30.69297324
95-th percentile1.4854043
Maximum1.9214062
Range9.9675914
Interquartile range (IQR)1.2457509

Descriptive statistics

Standard deviation1.0000331
Coefficient of variation (CV)3.3250331 × 1015
Kurtosis2.403795
Mean3.0075883 × 10-16
Median Absolute Deviation (MAD)0.62318913
Skewness-0.82931119
Sum4.8601123 × 10-12
Variance1.0000661
MonotonicityNot monotonic
2024-02-20T23:32:29.963028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.37947832 75
 
0.5%
-1.85144671 53
 
0.4%
-0.7860383551 47
 
0.3%
-0.3620473187 46
 
0.3%
-1.304899623 43
 
0.3%
-0.567717547 42
 
0.3%
-1.690135643 41
 
0.3%
-1.600357569 41
 
0.3%
-2.629636758 39
 
0.3%
-0.1254181467 39
 
0.3%
Other values (3264) 14654
96.9%
ValueCountFrequency (%)
-8.046185201 8
 
0.1%
-4.167709303 17
0.1%
-3.798142582 4
 
< 0.1%
-3.403207737 19
0.1%
-3.28051254 9
 
0.1%
-3.015275765 14
 
0.1%
-2.951448723 25
0.2%
-2.891036565 22
0.1%
-2.747598279 31
0.2%
-2.629636758 39
0.3%
ValueCountFrequency (%)
1.92140619 1
< 0.1%
1.914913713 1
< 0.1%
1.907039531 1
< 0.1%
1.892658056 1
< 0.1%
1.886340221 1
< 0.1%
1.882738639 1
< 0.1%
1.879814637 1
< 0.1%
1.873077891 1
< 0.1%
1.870476097 1
< 0.1%
1.861934894 1
< 0.1%

Horizontal_Distance_To_Fire_Points_log
Real number (ℝ)

HIGH CORRELATION 

Distinct2764
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5113824 × 10-16
Minimum-9.0889139
Maximum2.3137807
Zeros0
Zeros (%)0.0%
Negative6979
Negative (%)46.2%
Memory size118.2 KiB
2024-02-20T23:32:30.115330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-9.0889139
5-th percentile-1.7715197
Q1-0.57426107
median0.098281391
Q30.68722903
95-th percentile1.4929612
Maximum2.3137807
Range11.402695
Interquartile range (IQR)1.2614901

Descriptive statistics

Standard deviation1.0000331
Coefficient of variation (CV)2.2166887 × 1015
Kurtosis1.3500004
Mean4.5113824 × 10-16
Median Absolute Deviation (MAD)0.62627856
Skewness-0.65965671
Sum6.9348971 × 10-12
Variance1.0000661
MonotonicityNot monotonic
2024-02-20T23:32:30.306370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9936532742 49
 
0.3%
-0.8228314971 47
 
0.3%
-0.4612296605 45
 
0.3%
-2.025230811 45
 
0.3%
-0.8458886938 45
 
0.3%
-1.608526113 44
 
0.3%
-0.2086104158 40
 
0.3%
-1.582011188 39
 
0.3%
-0.3806904084 39
 
0.3%
-0.3400940358 38
 
0.3%
Other values (2754) 14689
97.1%
ValueCountFrequency (%)
-9.088913929 1
 
< 0.1%
-4.673052629 18
0.1%
-4.252280289 7
 
< 0.1%
-3.802624923 7
 
< 0.1%
-3.662929594 20
0.1%
-3.36094272 6
 
< 0.1%
-3.28827207 14
0.1%
-3.219489456 21
0.1%
-3.056176961 24
0.2%
-2.921871185 10
0.1%
ValueCountFrequency (%)
2.313780682 1
< 0.1%
2.307604436 1
< 0.1%
2.28926516 1
< 0.1%
2.28908044 1
< 0.1%
2.279065949 1
< 0.1%
2.277016363 1
< 0.1%
2.274403066 1
< 0.1%
2.261256321 1
< 0.1%
2.261067533 1
< 0.1%
2.260878718 1
< 0.1%

Distance_To_Hydrology_log
Real number (ℝ)

Distinct7027
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0151766 × 10-17
Minimum-2.6390437
Maximum1.4171034
Zeros0
Zeros (%)0.0%
Negative5168
Negative (%)34.2%
Memory size118.2 KiB
2024-02-20T23:32:30.456256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2.6390437
5-th percentile-2.6390437
Q1-0.26850257
median0.3006351
Q30.63701952
95-th percentile0.99037526
Maximum1.4171034
Range4.0561471
Interquartile range (IQR)0.90552209

Descriptive statistics

Standard deviation1.0000331
Coefficient of variation (CV)1.6625166 × 1016
Kurtosis2.0391142
Mean6.0151766 × 10-17
Median Absolute Deviation (MAD)0.40294086
Skewness-1.6573145
Sum-1.8189894 × 10-12
Variance1.0000661
MonotonicityNot monotonic
2024-02-20T23:32:30.620778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.639043686 1506
 
10.0%
-0.714762369 131
 
0.9%
-0.7144612628 121
 
0.8%
-0.7073316763 118
 
0.8%
-0.7120641837 110
 
0.7%
-0.7041245781 103
 
0.7%
-0.7135599132 98
 
0.6%
-0.7099837064 92
 
0.6%
-0.7003818544 81
 
0.5%
-0.6961255305 59
 
0.4%
Other values (7017) 12701
84.0%
ValueCountFrequency (%)
-2.639043686 1506
10.0%
-0.714762369 131
 
0.9%
-0.7144612628 121
 
0.8%
-0.7135599132 98
 
0.6%
-0.7120641837 110
 
0.7%
-0.7099837064 92
 
0.6%
-0.7073316763 118
 
0.8%
-0.7041245781 103
 
0.7%
-0.7003818544 81
 
0.5%
-0.6961255305 59
 
0.4%
ValueCountFrequency (%)
1.417103384 1
< 0.1%
1.407971821 1
< 0.1%
1.379903314 1
< 0.1%
1.368417834 1
< 0.1%
1.366982145 1
< 0.1%
1.354812602 1
< 0.1%
1.35260429 1
< 0.1%
1.34518376 1
< 0.1%
1.344793583 1
< 0.1%
1.343698037 1
< 0.1%

Wilderness_Area_2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14551 
1.0
 
569

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14551
96.2%
1.0 569
 
3.8%

Length

2024-02-20T23:32:30.852756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:31.044760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14551
96.2%
1.0 569
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 29671
65.4%
. 15120
33.3%
1 569
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29671
98.1%
1 569
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29671
65.4%
. 15120
33.3%
1 569
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29671
65.4%
. 15120
33.3%
1 569
 
1.3%

Wilderness_Area_3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
8818 
1.0
6302 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8818
58.3%
1.0 6302
41.7%

Length

2024-02-20T23:32:31.228285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:31.343337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8818
58.3%
1.0 6302
41.7%

Most occurring characters

ValueCountFrequency (%)
0 23938
52.8%
. 15120
33.3%
1 6302
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23938
79.2%
1 6302
 
20.8%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23938
52.8%
. 15120
33.3%
1 6302
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23938
52.8%
. 15120
33.3%
1 6302
 
13.9%

Wilderness_Area_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
10439 
1.0
4681 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 10439
69.0%
1.0 4681
31.0%

Length

2024-02-20T23:32:31.458800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:31.573223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10439
69.0%
1.0 4681
31.0%

Most occurring characters

ValueCountFrequency (%)
0 25559
56.3%
. 15120
33.3%
1 4681
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25559
84.5%
1 4681
 
15.5%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25559
56.3%
. 15120
33.3%
1 4681
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25559
56.3%
. 15120
33.3%
1 4681
 
10.3%

Climatic_Zone_3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15117 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15117
> 99.9%
1.0 3
 
< 0.1%

Length

2024-02-20T23:32:31.706248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:31.816188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15117
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30237
66.7%
. 15120
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30237
> 99.9%
1 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30237
66.7%
. 15120
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30237
66.7%
. 15120
33.3%
1 3
 
< 0.1%

Climatic_Zone_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
11871 
1.0
3249 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11871
78.5%
1.0 3249
 
21.5%

Length

2024-02-20T23:32:31.937441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:32.047975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11871
78.5%
1.0 3249
 
21.5%

Most occurring characters

ValueCountFrequency (%)
0 26991
59.5%
. 15120
33.3%
1 3249
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26991
89.3%
1 3249
 
10.7%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26991
59.5%
. 15120
33.3%
1 3249
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26991
59.5%
. 15120
33.3%
1 3249
 
7.2%

Climatic_Zone_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14947 
1.0
 
173

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14947
98.9%
1.0 173
 
1.1%

Length

2024-02-20T23:32:32.154467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:32.280118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14947
98.9%
1.0 173
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 30067
66.3%
. 15120
33.3%
1 173
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30067
99.4%
1 173
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30067
66.3%
. 15120
33.3%
1 173
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30067
66.3%
. 15120
33.3%
1 173
 
0.4%

Climatic_Zone_6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14330 
1.0
 
790

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 14330
94.8%
1.0 790
 
5.2%

Length

2024-02-20T23:32:32.393387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:32.519802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14330
94.8%
1.0 790
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 29450
64.9%
. 15120
33.3%
1 790
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29450
97.4%
1 790
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29450
64.9%
. 15120
33.3%
1 790
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29450
64.9%
. 15120
33.3%
1 790
 
1.7%

Climatic_Zone_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
9869 
1.0
5251 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9869
65.3%
1.0 5251
34.7%

Length

2024-02-20T23:32:32.656246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:32.777129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9869
65.3%
1.0 5251
34.7%

Most occurring characters

ValueCountFrequency (%)
0 24989
55.1%
. 15120
33.3%
1 5251
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24989
82.6%
1 5251
 
17.4%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24989
55.1%
. 15120
33.3%
1 5251
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24989
55.1%
. 15120
33.3%
1 5251
 
11.6%

Climatic_Zone_8
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
13137 
1.0
1983 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13137
86.9%
1.0 1983
 
13.1%

Length

2024-02-20T23:32:32.914433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:33.026791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13137
86.9%
1.0 1983
 
13.1%

Most occurring characters

ValueCountFrequency (%)
0 28257
62.3%
. 15120
33.3%
1 1983
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28257
93.4%
1 1983
 
6.6%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28257
62.3%
. 15120
33.3%
1 1983
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28257
62.3%
. 15120
33.3%
1 1983
 
4.4%

Geologic_Zone_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14042 
1.0
 
1078

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14042
92.9%
1.0 1078
 
7.1%

Length

2024-02-20T23:32:33.146868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:33.259016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14042
92.9%
1.0 1078
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 29162
64.3%
. 15120
33.3%
1 1078
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29162
96.4%
1 1078
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29162
64.3%
. 15120
33.3%
1 1078
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29162
64.3%
. 15120
33.3%
1 1078
 
2.4%

Geologic_Zone_5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15117 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15117
> 99.9%
1.0 3
 
< 0.1%

Length

2024-02-20T23:32:33.551919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:33.678839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15117
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30237
66.7%
. 15120
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30237
> 99.9%
1 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30237
66.7%
. 15120
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30237
66.7%
. 15120
33.3%
1 3
 
< 0.1%

Geologic_Zone_7
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
1.0
12925 
0.0
2195 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 12925
85.5%
0.0 2195
 
14.5%

Length

2024-02-20T23:32:33.798533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:33.913664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12925
85.5%
0.0 2195
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 17315
38.2%
. 15120
33.3%
1 12925
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17315
57.3%
1 12925
42.7%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17315
38.2%
. 15120
33.3%
1 12925
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17315
38.2%
. 15120
33.3%
1 12925
28.5%

Soil_Type_2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14493 
1.0
 
627

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14493
95.9%
1.0 627
 
4.1%

Length

2024-02-20T23:32:34.047498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:34.166320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14493
95.9%
1.0 627
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 29613
65.3%
. 15120
33.3%
1 627
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29613
97.9%
1 627
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29613
65.3%
. 15120
33.3%
1 627
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29613
65.3%
. 15120
33.3%
1 627
 
1.4%

Soil_Type_3
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14114 
1.0
 
1006

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14114
93.3%
1.0 1006
 
6.7%

Length

2024-02-20T23:32:34.291630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:34.407320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14114
93.3%
1.0 1006
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 29234
64.4%
. 15120
33.3%
1 1006
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29234
96.7%
1 1006
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29234
64.4%
. 15120
33.3%
1 1006
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29234
64.4%
. 15120
33.3%
1 1006
 
2.2%

Soil_Type_4
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14281 
1.0
 
839

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14281
94.5%
1.0 839
 
5.5%

Length

2024-02-20T23:32:34.522111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:34.626908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14281
94.5%
1.0 839
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 29401
64.8%
. 15120
33.3%
1 839
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29401
97.2%
1 839
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29401
64.8%
. 15120
33.3%
1 839
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29401
64.8%
. 15120
33.3%
1 839
 
1.8%

Soil_Type_5
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14939 
1.0
 
181

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14939
98.8%
1.0 181
 
1.2%

Length

2024-02-20T23:32:34.747790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:34.860140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14939
98.8%
1.0 181
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 30059
66.3%
. 15120
33.3%
1 181
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30059
99.4%
1 181
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30059
66.3%
. 15120
33.3%
1 181
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30059
66.3%
. 15120
33.3%
1 181
 
0.4%

Soil_Type_6
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14441 
1.0
 
679

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14441
95.5%
1.0 679
 
4.5%

Length

2024-02-20T23:32:34.972164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:35.092948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14441
95.5%
1.0 679
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 29561
65.2%
. 15120
33.3%
1 679
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29561
97.8%
1 679
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29561
65.2%
. 15120
33.3%
1 679
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29561
65.2%
. 15120
33.3%
1 679
 
1.5%

Soil_Type_7
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15119 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15119
> 99.9%
1.0 1
 
< 0.1%

Length

2024-02-20T23:32:35.213065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:35.325717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15119
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30239
66.7%
. 15120
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30239
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30239
66.7%
. 15120
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30239
66.7%
. 15120
33.3%
1 1
 
< 0.1%

Soil_Type_8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15118 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15118
> 99.9%
1.0 2
 
< 0.1%

Length

2024-02-20T23:32:35.448287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:35.574629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15118
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30238
66.7%
. 15120
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30238
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30238
66.7%
. 15120
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30238
66.7%
. 15120
33.3%
1 2
 
< 0.1%

Soil_Type_9
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15116 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15116
> 99.9%
1.0 4
 
< 0.1%

Length

2024-02-20T23:32:35.753026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:35.929474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15116
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30236
66.7%
. 15120
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30236
> 99.9%
1 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30236
66.7%
. 15120
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30236
66.7%
. 15120
33.3%
1 4
 
< 0.1%

Soil_Type_10
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
13024 
1.0
2096 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13024
86.1%
1.0 2096
 
13.9%

Length

2024-02-20T23:32:36.068868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:36.344388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13024
86.1%
1.0 2096
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 28144
62.0%
. 15120
33.3%
1 2096
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28144
93.1%
1 2096
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28144
62.0%
. 15120
33.3%
1 2096
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28144
62.0%
. 15120
33.3%
1 2096
 
4.6%

Soil_Type_11
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14744 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14744
97.5%
1.0 376
 
2.5%

Length

2024-02-20T23:32:36.465334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:36.578092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14744
97.5%
1.0 376
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 29864
65.8%
. 15120
33.3%
1 376
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29864
98.8%
1 376
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29864
65.8%
. 15120
33.3%
1 376
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29864
65.8%
. 15120
33.3%
1 376
 
0.8%

Soil_Type_12
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14860 
1.0
 
260

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14860
98.3%
1.0 260
 
1.7%

Length

2024-02-20T23:32:36.722278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:36.852057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14860
98.3%
1.0 260
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 29980
66.1%
. 15120
33.3%
1 260
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29980
99.1%
1 260
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29980
66.1%
. 15120
33.3%
1 260
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29980
66.1%
. 15120
33.3%
1 260
 
0.6%

Soil_Type_13
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14607 
1.0
 
513

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14607
96.6%
1.0 513
 
3.4%

Length

2024-02-20T23:32:36.985943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:37.093414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14607
96.6%
1.0 513
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 29727
65.5%
. 15120
33.3%
1 513
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29727
98.3%
1 513
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29727
65.5%
. 15120
33.3%
1 513
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29727
65.5%
. 15120
33.3%
1 513
 
1.1%

Soil_Type_14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14947 
1.0
 
173

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14947
98.9%
1.0 173
 
1.1%

Length

2024-02-20T23:32:37.208259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:37.335441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14947
98.9%
1.0 173
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 30067
66.3%
. 15120
33.3%
1 173
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30067
99.4%
1 173
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30067
66.3%
. 15120
33.3%
1 173
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30067
66.3%
. 15120
33.3%
1 173
 
0.4%

Soil_Type_16
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15014 
1.0
 
106

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15014
99.3%
1.0 106
 
0.7%

Length

2024-02-20T23:32:37.501776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:37.693676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15014
99.3%
1.0 106
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 30134
66.4%
. 15120
33.3%
1 106
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30134
99.6%
1 106
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30134
66.4%
. 15120
33.3%
1 106
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30134
66.4%
. 15120
33.3%
1 106
 
0.2%

Soil_Type_17
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14480 
1.0
 
640

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 14480
95.8%
1.0 640
 
4.2%

Length

2024-02-20T23:32:37.838351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:37.950283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14480
95.8%
1.0 640
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 29600
65.3%
. 15120
33.3%
1 640
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29600
97.9%
1 640
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29600
65.3%
. 15120
33.3%
1 640
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29600
65.3%
. 15120
33.3%
1 640
 
1.4%

Soil_Type_18
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15076 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15076
99.7%
1.0 44
 
0.3%

Length

2024-02-20T23:32:38.062836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:38.205030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15076
99.7%
1.0 44
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 30196
66.6%
. 15120
33.3%
1 44
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30196
99.9%
1 44
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30196
66.6%
. 15120
33.3%
1 44
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30196
66.6%
. 15120
33.3%
1 44
 
0.1%

Soil_Type_19
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15067 
1.0
 
53

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15067
99.6%
1.0 53
 
0.4%

Length

2024-02-20T23:32:38.394543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:38.574480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15067
99.6%
1.0 53
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 30187
66.5%
. 15120
33.3%
1 53
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30187
99.8%
1 53
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30187
66.5%
. 15120
33.3%
1 53
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30187
66.5%
. 15120
33.3%
1 53
 
0.1%

Soil_Type_20
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14988 
1.0
 
132

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14988
99.1%
1.0 132
 
0.9%

Length

2024-02-20T23:32:38.695166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:38.815477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14988
99.1%
1.0 132
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 30108
66.4%
. 15120
33.3%
1 132
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30108
99.6%
1 132
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30108
66.4%
. 15120
33.3%
1 132
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30108
66.4%
. 15120
33.3%
1 132
 
0.3%

Soil_Type_21
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15110 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15110
99.9%
1.0 10
 
0.1%

Length

2024-02-20T23:32:38.977204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:39.147735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15110
99.9%
1.0 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 30230
66.6%
. 15120
33.3%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30230
> 99.9%
1 10
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30230
66.6%
. 15120
33.3%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30230
66.6%
. 15120
33.3%
1 10
 
< 0.1%

Soil_Type_22
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14788 
1.0
 
332

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14788
97.8%
1.0 332
 
2.2%

Length

2024-02-20T23:32:39.292669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:39.397114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14788
97.8%
1.0 332
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 29908
65.9%
. 15120
33.3%
1 332
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29908
98.9%
1 332
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29908
65.9%
. 15120
33.3%
1 332
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29908
65.9%
. 15120
33.3%
1 332
 
0.7%

Soil_Type_23
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14378 
1.0
 
742

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14378
95.1%
1.0 742
 
4.9%

Length

2024-02-20T23:32:39.525929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:39.680362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14378
95.1%
1.0 742
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 29498
65.0%
. 15120
33.3%
1 742
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29498
97.5%
1 742
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29498
65.0%
. 15120
33.3%
1 742
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29498
65.0%
. 15120
33.3%
1 742
 
1.6%

Soil_Type_24
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14855 
1.0
 
265

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14855
98.2%
1.0 265
 
1.8%

Length

2024-02-20T23:32:39.840943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:39.951516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14855
98.2%
1.0 265
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 29975
66.1%
. 15120
33.3%
1 265
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29975
99.1%
1 265
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29975
66.1%
. 15120
33.3%
1 265
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29975
66.1%
. 15120
33.3%
1 265
 
0.6%

Soil_Type_25
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15114 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15114
> 99.9%
1.0 6
 
< 0.1%

Length

2024-02-20T23:32:40.080352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:40.191006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15114
> 99.9%
1.0 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30234
66.7%
. 15120
33.3%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30234
> 99.9%
1 6
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30234
66.7%
. 15120
33.3%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30234
66.7%
. 15120
33.3%
1 6
 
< 0.1%

Soil_Type_26
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15072 
1.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15072
99.7%
1.0 48
 
0.3%

Length

2024-02-20T23:32:40.307653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:40.420135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15072
99.7%
1.0 48
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 30192
66.6%
. 15120
33.3%
1 48
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30192
99.8%
1 48
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30192
66.6%
. 15120
33.3%
1 48
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30192
66.6%
. 15120
33.3%
1 48
 
0.1%

Soil_Type_27
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15112 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15112
99.9%
1.0 8
 
0.1%

Length

2024-02-20T23:32:40.524271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:40.641054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15112
99.9%
1.0 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 30232
66.6%
. 15120
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30232
> 99.9%
1 8
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30232
66.6%
. 15120
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30232
66.6%
. 15120
33.3%
1 8
 
< 0.1%

Soil_Type_28
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15113 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15113
> 99.9%
1.0 7
 
< 0.1%

Length

2024-02-20T23:32:40.762166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:40.876958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15113
> 99.9%
1.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 30233
66.7%
. 15120
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30233
> 99.9%
1 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30233
66.7%
. 15120
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30233
66.7%
. 15120
33.3%
1 7
 
< 0.1%

Soil_Type_29
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
13812 
1.0
 
1308

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13812
91.3%
1.0 1308
 
8.7%

Length

2024-02-20T23:32:40.990952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:41.240951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13812
91.3%
1.0 1308
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 28932
63.8%
. 15120
33.3%
1 1308
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28932
95.7%
1 1308
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28932
63.8%
. 15120
33.3%
1 1308
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28932
63.8%
. 15120
33.3%
1 1308
 
2.9%

Soil_Type_30
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14384 
1.0
 
736

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14384
95.1%
1.0 736
 
4.9%

Length

2024-02-20T23:32:41.357646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:41.473947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14384
95.1%
1.0 736
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 29504
65.0%
. 15120
33.3%
1 736
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29504
97.6%
1 736
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29504
65.0%
. 15120
33.3%
1 736
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29504
65.0%
. 15120
33.3%
1 736
 
1.6%

Soil_Type_31
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14816 
1.0
 
304

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14816
98.0%
1.0 304
 
2.0%

Length

2024-02-20T23:32:41.590880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:41.690936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14816
98.0%
1.0 304
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 29936
66.0%
. 15120
33.3%
1 304
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29936
99.0%
1 304
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29936
66.0%
. 15120
33.3%
1 304
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29936
66.0%
. 15120
33.3%
1 304
 
0.7%

Soil_Type_32
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14457 
1.0
 
663

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14457
95.6%
1.0 663
 
4.4%

Length

2024-02-20T23:32:41.807690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:41.924240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14457
95.6%
1.0 663
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 29577
65.2%
. 15120
33.3%
1 663
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29577
97.8%
1 663
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29577
65.2%
. 15120
33.3%
1 663
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29577
65.2%
. 15120
33.3%
1 663
 
1.5%

Soil_Type_33
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14501 
1.0
 
619

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14501
95.9%
1.0 619
 
4.1%

Length

2024-02-20T23:32:42.043562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:42.156489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14501
95.9%
1.0 619
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 29621
65.3%
. 15120
33.3%
1 619
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29621
98.0%
1 619
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29621
65.3%
. 15120
33.3%
1 619
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29621
65.3%
. 15120
33.3%
1 619
 
1.4%

Soil_Type_34
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15102 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15102
99.9%
1.0 18
 
0.1%

Length

2024-02-20T23:32:42.260326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:42.376515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15102
99.9%
1.0 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 30222
66.6%
. 15120
33.3%
1 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30222
99.9%
1 18
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30222
66.6%
. 15120
33.3%
1 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30222
66.6%
. 15120
33.3%
1 18
 
< 0.1%

Soil_Type_35
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15017 
1.0
 
103

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15017
99.3%
1.0 103
 
0.7%

Length

2024-02-20T23:32:42.490877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:42.607888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15017
99.3%
1.0 103
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 30137
66.4%
. 15120
33.3%
1 103
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30137
99.7%
1 103
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30137
66.4%
. 15120
33.3%
1 103
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30137
66.4%
. 15120
33.3%
1 103
 
0.2%

Soil_Type_36
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15106 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15106
99.9%
1.0 14
 
0.1%

Length

2024-02-20T23:32:42.725217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:42.841122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15106
99.9%
1.0 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 30226
66.6%
. 15120
33.3%
1 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30226
> 99.9%
1 14
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30226
66.6%
. 15120
33.3%
1 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30226
66.6%
. 15120
33.3%
1 14
 
< 0.1%

Soil_Type_37
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
15088 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15088
99.8%
1.0 32
 
0.2%

Length

2024-02-20T23:32:42.957510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:43.066529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15088
99.8%
1.0 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 30208
66.6%
. 15120
33.3%
1 32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30208
99.9%
1 32
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30208
66.6%
. 15120
33.3%
1 32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30208
66.6%
. 15120
33.3%
1 32
 
0.1%

Soil_Type_38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14376 
1.0
 
744

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14376
95.1%
1.0 744
 
4.9%

Length

2024-02-20T23:32:43.178875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:43.290852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14376
95.1%
1.0 744
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 29496
65.0%
. 15120
33.3%
1 744
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29496
97.5%
1 744
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29496
65.0%
. 15120
33.3%
1 744
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29496
65.0%
. 15120
33.3%
1 744
 
1.6%

Soil_Type_39
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14486 
1.0
 
634

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14486
95.8%
1.0 634
 
4.2%

Length

2024-02-20T23:32:43.407484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:43.519029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14486
95.8%
1.0 634
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 29606
65.3%
. 15120
33.3%
1 634
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29606
97.9%
1 634
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29606
65.3%
. 15120
33.3%
1 634
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29606
65.3%
. 15120
33.3%
1 634
 
1.4%

Soil_Type_40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
0.0
14664 
1.0
 
456

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14664
97.0%
1.0 456
 
3.0%

Length

2024-02-20T23:32:43.626326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-20T23:32:43.743482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14664
97.0%
1.0 456
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 29784
65.7%
. 15120
33.3%
1 456
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29784
98.5%
1 456
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29784
65.7%
. 15120
33.3%
1 456
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29784
65.7%
. 15120
33.3%
1 456
 
1.0%

Interactions

2024-02-20T23:32:24.482828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.128858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.295875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.541763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.713142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.011140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.317250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.643739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.970637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.158452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.611073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.244371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.416667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.653999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.832771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.116453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.447621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.758724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.058818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.263255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.762122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.358770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.525612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.758218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.952799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.229889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.563956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.874885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.178784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.527533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.876038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.470634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.643776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.886322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.169712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.355482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.697459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.991843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.278461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.637770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.995299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.581876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.758920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.990759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.281781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.467173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.813726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.133237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.391378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.744116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:25.119374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.686197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.893530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.103400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.386105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.584791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.941903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.261991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.520880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.862936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:25.263688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.806833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.018617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.227930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.514409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.700725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.089213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.396751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.641831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.991862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:25.413180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:13.927426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.159006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.368017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.642085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:18.844882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.214049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.535790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.775822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.126708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:25.536637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.039566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.279238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.480432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.758170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.069182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.341933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.675397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:22.896628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.244562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:25.669852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:14.159846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:15.428268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:16.600809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:17.869682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:19.181215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:20.495448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:21.801934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:23.008476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-20T23:32:24.357891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-20T23:32:43.917001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Aspect_cosAspect_sinClimatic_Zone_3Climatic_Zone_4Climatic_Zone_5Climatic_Zone_6Climatic_Zone_7Climatic_Zone_8Distance_To_Hydrology_logElevationGeologic_Zone_2Geologic_Zone_5Geologic_Zone_7Hillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_Points_logHorizontal_Distance_To_Roadways_logSlopeSoil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_2Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_3Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_4Soil_Type_40Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Wilderness_Area_2Wilderness_Area_3Wilderness_Area_4
Aspect_cos1.0000.0030.0000.0150.0120.0190.0000.0000.0160.0120.0000.0000.000-0.008-0.003-0.0150.0150.0000.0010.0000.0000.0000.0000.0120.0000.0170.0000.0000.0000.0180.0000.0000.0000.0140.0180.0140.0000.0000.0150.0190.0000.0120.0000.0130.0230.0000.0000.0070.0130.0000.0230.0190.0110.0180.0000.0000.0000.0210.0000.000
Aspect_sin0.0031.0000.0110.0000.0190.0130.0000.010-0.022-0.0200.0140.0110.012-0.0090.013-0.0020.0040.0030.0090.0130.0000.0000.0000.0190.0090.0190.0000.0000.0140.0000.0050.0000.0170.0300.0000.0070.0220.0000.0180.0050.0000.0000.0130.0000.0000.0230.0090.0110.0120.0070.0340.0260.0000.0010.0200.0100.0180.0220.0000.005
Climatic_Zone_30.0000.0111.0000.0000.0000.0000.0000.0000.0100.0070.0000.8330.0260.0020.0030.0080.0210.022-0.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2880.6120.0000.0000.0000.000
Climatic_Zone_40.0150.0000.0001.0000.0550.1220.3810.203-0.027-0.2870.1420.0000.2130.101-0.259-0.216-0.157-0.1540.1940.7670.3050.2520.3580.0550.0420.1090.0260.0290.1080.0480.0060.0770.1180.0690.0000.0270.0030.0000.1610.1390.1180.0740.1110.1070.0130.0420.0100.0210.1180.1090.1260.0910.0560.1130.0000.0000.0250.1010.0290.184
Climatic_Zone_50.0120.0190.0000.0551.0000.0220.0770.040-0.159-0.1410.0270.0000.2600.017-0.0160.003-0.104-0.090-0.0400.0410.0130.0090.0170.9970.0000.0190.0000.0000.0190.0000.0000.0110.0220.0090.0000.0000.0000.0000.0310.0260.0210.0100.0200.0190.0000.0000.0000.0000.0220.0190.0230.0150.0040.0200.0000.0000.0000.0180.0530.121
Climatic_Zone_60.0190.0130.0000.1220.0221.0000.1710.090-0.260-0.2100.0640.0000.5320.094-0.0500.107-0.017-0.055-0.1540.0930.0360.0290.0420.0220.3560.8950.2270.0080.0470.0190.0000.0330.0520.0290.0000.0070.0000.0000.0710.0620.0520.0320.0490.0470.0000.0160.0000.0000.0520.0480.0560.0400.0230.0500.0000.0000.0000.0420.0260.124
Climatic_Zone_70.0000.0000.0000.3810.0770.1711.0000.2830.0910.4450.3770.0000.1990.0080.0360.0700.3010.244-0.1870.2920.1160.0960.1360.0770.0600.1530.0370.0800.1510.1280.0320.2050.3110.1820.0220.0760.0270.0250.4220.1940.3100.1960.2930.2830.0450.0590.0180.0310.1650.1520.1760.1280.0790.1580.0000.0000.0000.0700.0250.488
Climatic_Zone_80.0000.0100.0000.2030.0400.0900.2831.0000.1880.5650.1070.0000.1600.0110.0120.0210.1720.322-0.1040.1550.0610.0500.0720.0400.0300.0810.0170.0200.0800.0340.0000.0570.0870.0500.0000.0180.0000.0000.1190.1030.0870.0540.0820.0790.0070.2120.0750.1160.5850.5380.0930.4530.0410.0830.0000.0000.0000.2020.1280.260
Distance_To_Hydrology_log0.016-0.0220.010-0.027-0.159-0.2600.0910.1881.0000.3760.1020.0000.3900.036-0.0330.0170.1670.1300.0370.1440.0370.0660.0690.2530.1740.3380.0410.0440.0900.1140.0390.0370.1200.0540.0210.0450.0750.0000.0900.0870.1030.0710.1380.0700.0750.0690.0970.0330.1630.0780.0570.2330.0310.0960.0000.0000.0160.0970.1960.275
Elevation0.012-0.0200.007-0.287-0.141-0.2100.4450.5650.3761.0000.3650.0270.2350.0820.0330.2060.5140.601-0.3160.4020.1700.1860.2510.1700.0870.2500.1490.0880.2560.1010.0320.2870.2580.1380.0280.0890.0320.0260.3410.3810.3150.1320.2350.1590.0470.1530.0730.1140.4890.4230.2400.6540.2470.3490.0000.0170.0380.3900.5260.923
Geologic_Zone_20.0000.0140.0000.1420.0270.0640.3770.1070.1020.3651.0000.0000.6720.098-0.0440.1250.1100.154-0.1990.1100.0430.0350.0510.0270.0200.0570.0100.0120.0560.0230.0000.5400.8190.0350.0000.0110.0000.0000.0840.0730.0620.0380.0580.0560.0000.0200.0000.0060.0620.0570.0660.0470.0280.0590.0000.0000.0500.1440.0190.185
Geologic_Zone_50.0000.0110.8330.0000.0000.0000.0000.0000.0000.0270.0001.0000.0260.0020.0030.0080.0210.022-0.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2880.6120.0000.0000.0000.000
Geologic_Zone_70.0000.0120.0260.2130.2600.5320.1990.1600.3900.2350.6720.0261.000-0.1490.077-0.170-0.044-0.0800.2770.1650.0650.0530.0760.2600.2030.5100.0190.1420.0850.2270.0580.3630.5510.0540.0000.0200.0000.0000.1260.1090.0920.0580.0870.0840.0080.0320.0050.0150.0930.0850.0990.0720.0440.0890.0010.0180.0330.0720.0300.039
Hillshade_3pm-0.008-0.0090.0020.1010.0170.0940.0080.0110.0360.0820.0980.002-0.1491.000-0.8370.5840.0200.179-0.2940.1870.1120.1000.1180.0260.0530.1070.0520.0410.0620.0620.0230.0530.1310.0370.0000.0530.0480.0620.0890.3840.2550.0660.1080.0840.0320.0380.0300.0250.1220.0970.0380.0680.0710.0290.0000.0000.0000.0600.1710.222
Hillshade_9am-0.0030.0130.003-0.259-0.016-0.0500.0360.012-0.0330.033-0.0440.0030.077-0.8371.000-0.1390.049-0.0580.0000.4080.0690.0900.1170.0170.0510.0960.0440.0300.0730.0420.0260.0460.1310.0660.0000.0600.0360.0590.1040.3160.2050.0780.0950.0620.0400.0270.0280.0350.1010.0630.0520.0760.1050.0280.0000.0000.0000.0210.1470.262
Hillshade_Noon-0.015-0.0020.008-0.2160.0030.1070.0700.0210.0170.2060.1250.008-0.1700.584-0.1391.0000.1080.256-0.5390.2950.0730.0940.0480.0290.0440.0930.0490.0440.1000.0480.0110.0440.1240.0950.0080.0760.0020.0430.0510.1170.1690.0570.1100.0460.0310.0360.0560.0210.1120.0990.1640.0510.1590.0450.0000.0000.0000.0740.2130.213
Horizontal_Distance_To_Fire_Points_log0.0150.0040.021-0.157-0.104-0.0170.3010.1720.1670.5140.1100.021-0.0440.0200.0490.1081.0000.432-0.2430.2290.0540.1960.0830.1000.0240.0720.1650.0140.1220.0630.0000.0500.0960.0530.0570.0560.0150.0000.2200.1950.0560.0350.0720.0540.0370.0510.0360.0450.1180.0580.0660.1640.0860.0910.0130.0000.0060.0550.1910.444
Horizontal_Distance_To_Roadways_log0.0000.0030.022-0.154-0.090-0.0550.2440.3220.1300.6010.1540.022-0.0800.179-0.0580.2560.4321.000-0.2900.2210.0850.1020.1570.0930.0330.1310.0750.0500.1190.0450.0170.1160.1320.0250.0250.0630.0160.0000.2810.1800.2640.0860.0860.0710.0310.0700.0550.0910.2050.1490.1110.1190.1390.1970.0000.0110.0130.1030.3330.468
Slope0.0010.009-0.0210.194-0.040-0.154-0.187-0.1040.037-0.316-0.199-0.0210.277-0.2940.000-0.539-0.243-0.2901.0000.2570.1020.1400.1360.0410.0560.1320.0680.0730.0880.0690.0080.0610.1980.0720.0000.0580.0120.0210.1210.2980.1410.0910.1500.1150.0300.0460.0280.0000.1740.0860.1080.0500.1120.0420.0000.0320.0000.0780.1490.305
Soil_Type_100.0000.0130.0000.7670.0410.0930.2920.1550.1440.4020.1100.0000.1650.1870.4080.2950.2290.2210.2571.0000.0630.0520.0740.0410.0320.0830.0180.0210.0830.0360.0000.0590.0900.0520.0000.0190.0000.0000.1230.1060.0900.0560.0850.0820.0080.0310.0040.0140.0900.0830.0960.0700.0430.0860.0000.0000.0000.0780.0950.339
Soil_Type_110.0000.0000.0000.3050.0130.0360.1160.0610.0370.1700.0430.0000.0650.1120.0690.0730.0540.0850.1020.0631.0000.0180.0280.0130.0070.0310.0000.0000.0310.0100.0000.0210.0340.0180.0000.0000.0000.0000.0480.0410.0340.0200.0320.0310.0000.0070.0000.0000.0340.0310.0370.0260.0130.0330.0000.0000.0000.0290.1310.044
Soil_Type_120.0000.0000.0000.2520.0090.0290.0960.0500.0660.1860.0350.0000.0530.1000.0900.0940.1960.1020.1400.0520.0181.0000.0220.0090.0000.0250.0000.0000.0250.0050.0000.0160.0280.0130.0000.0000.0000.0000.0390.0330.0280.0150.0260.0250.0000.0000.0000.0000.0280.0250.0300.0200.0090.0260.0000.0000.0000.0230.1110.088
Soil_Type_130.0000.0000.0000.3580.0170.0420.1360.0720.0690.2510.0510.0000.0760.1180.1170.0480.0830.1570.1360.0740.0280.0221.0000.0170.0110.0380.0000.0000.0370.0130.0000.0260.0410.0220.0000.0000.0000.0000.0560.0490.0410.0240.0380.0370.0000.0110.0000.0000.0410.0370.0440.0310.0170.0390.0000.0000.0000.0310.2200.125
Soil_Type_140.0120.0190.0000.0550.9970.0220.0770.0400.2530.1700.0270.0000.2600.0260.0170.0290.1000.0930.0410.0410.0130.0090.0171.0000.0000.0190.0000.0000.0190.0000.0000.0110.0220.0090.0000.0000.0000.0000.0310.0260.0210.0100.0200.0190.0000.0000.0000.0000.0220.0190.0230.0150.0040.0200.0000.0000.0000.0180.0530.121
Soil_Type_160.0000.0090.0000.0420.0000.3560.0600.0300.1740.0870.0200.0000.2030.0530.0510.0440.0240.0330.0560.0320.0070.0000.0110.0001.0000.0130.0000.0000.0130.0000.0000.0060.0150.0010.0000.0000.0000.0000.0230.0190.0150.0040.0140.0130.0000.0000.0000.0000.0150.0130.0170.0090.0000.0140.0000.0000.0000.0000.0360.047
Soil_Type_170.0170.0190.0000.1090.0190.8950.1530.0810.3380.2500.0570.0000.5100.1070.0960.0930.0720.1310.1320.0830.0310.0250.0380.0190.0131.0000.0020.0050.0420.0160.0000.0290.0460.0260.0000.0040.0000.0000.0640.0550.0460.0280.0430.0420.0000.0130.0000.0000.0460.0420.0500.0350.0200.0440.0000.0000.0000.0400.0000.126
Soil_Type_180.0000.0000.0000.0260.0000.2270.0370.0170.0410.1490.0100.0000.0190.0520.0440.0490.1650.0750.0680.0180.0000.0000.0000.0000.0000.0021.0000.0000.0010.0000.0000.0000.0050.0000.0000.0000.0000.0000.0120.0090.0050.0000.0030.0000.0000.0000.0000.0000.0050.0010.0070.0000.0000.0030.0000.0000.0000.0000.0440.034
Soil_Type_190.0000.0000.0000.0290.0000.0080.0800.0200.0440.0880.0120.0000.1420.0410.0300.0440.0140.0500.0730.0210.0000.0000.0000.0000.0000.0050.0001.0000.0050.0000.0000.0000.0070.0000.0000.0000.0000.0000.0140.0110.0070.0000.0060.0050.0000.0000.0000.0000.0070.0050.0090.0000.0000.0060.0000.0000.0000.0250.0000.038
Soil_Type_20.0000.0140.0000.1080.0190.0470.1510.0800.0900.2560.0560.0000.0850.0620.0730.1000.1220.1190.0880.0830.0310.0250.0370.0190.0130.0420.0010.0051.0000.0160.0000.0290.0460.0250.0000.0030.0000.0000.0630.0540.0460.0270.0430.0410.0000.0130.0000.0000.0460.0420.0490.0350.0200.0440.0000.0000.0000.0390.0640.053
Soil_Type_200.0180.0000.0000.0480.0000.0190.1280.0340.1140.1010.0230.0000.2270.0620.0420.0480.0630.0450.0690.0360.0100.0050.0130.0000.0000.0160.0000.0000.0161.0000.0000.0080.0180.0060.0000.0000.0000.0000.0260.0220.0180.0070.0160.0160.0000.0000.0000.0000.0180.0160.0200.0120.0000.0170.0000.0000.0000.0150.0340.062
Soil_Type_210.0000.0050.0000.0060.0000.0000.0320.0000.0390.0320.0000.0000.0580.0230.0260.0110.0000.0170.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.012
Soil_Type_220.0000.0000.0000.0770.0110.0330.2050.0570.0370.2870.5400.0000.3630.0530.0460.0440.0500.1160.0610.0590.0210.0160.0260.0110.0060.0290.0000.0000.0290.0080.0001.0000.0320.0160.0000.0000.0000.0000.0450.0380.0320.0180.0300.0290.0000.0050.0000.0000.0320.0290.0340.0240.0120.0300.0000.0000.0000.0870.0510.100
Soil_Type_230.0000.0170.0000.1180.0220.0520.3110.0870.1200.2580.8190.0000.5510.1310.1310.1240.0960.1320.1980.0900.0340.0280.0410.0220.0150.0460.0050.0070.0460.0180.0000.0321.0000.0280.0000.0060.0000.0000.0690.0590.0500.0300.0470.0450.0000.0150.0000.0000.0500.0460.0540.0380.0220.0480.0000.0000.0000.1120.0070.152
Soil_Type_240.0140.0300.0000.0690.0090.0290.1820.0500.0540.1380.0350.0000.0540.0370.0660.0950.0530.0250.0720.0520.0180.0130.0220.0090.0010.0260.0000.0000.0250.0060.0000.0160.0281.0000.0000.0000.0000.0000.0390.0340.0280.0150.0260.0250.0000.0000.0000.0000.0280.0250.0300.0210.0090.0270.0000.0000.0000.0830.0760.089
Soil_Type_250.0180.0000.0000.0000.0000.0000.0220.0000.0210.0280.0000.0000.0000.0000.0000.0080.0570.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0110.005
Soil_Type_260.0140.0070.0000.0270.0000.0070.0760.0180.0450.0890.0110.0000.0200.0530.0600.0760.0560.0630.0580.0190.0000.0000.0000.0000.0000.0040.0000.0000.0030.0000.0000.0000.0060.0000.0001.0000.0000.0000.0130.0100.0060.0000.0040.0030.0000.0000.0000.0000.0060.0040.0080.0000.0000.0050.0000.0000.0000.0000.0650.036
Soil_Type_270.0000.0220.0000.0030.0000.0000.0270.0000.0750.0320.0000.0000.0000.0480.0360.0020.0150.0160.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.009
Soil_Type_280.0000.0000.0000.0000.0000.0000.0250.0000.0000.0260.0000.0000.0000.0620.0590.0430.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.008
Soil_Type_290.0150.0180.0000.1610.0310.0710.4220.1190.0900.3410.0840.0000.1260.0890.1040.0510.2200.2810.1210.1230.0480.0390.0560.0310.0230.0640.0120.0140.0630.0260.0000.0450.0690.0390.0000.0130.0000.0001.0000.0810.0690.0420.0650.0620.0000.0230.0000.0080.0690.0630.0740.0530.0320.0660.0000.0000.0000.0560.2600.206
Soil_Type_30.0190.0050.0000.1390.0260.0620.1940.1030.0870.3810.0730.0000.1090.3840.3160.1170.1950.1800.2980.1060.0410.0330.0490.0260.0190.0550.0090.0110.0540.0220.0000.0380.0590.0340.0000.0100.0000.0000.0811.0000.0590.0360.0560.0540.0000.0190.0000.0050.0600.0550.0640.0460.0270.0570.0000.0000.0000.0510.1690.339
Soil_Type_300.0000.0000.0000.1180.0210.0520.3100.0870.1030.3150.0620.0000.0920.2550.2050.1690.0560.2640.1410.0900.0340.0280.0410.0210.0150.0460.0050.0070.0460.0180.0000.0320.0500.0280.0000.0060.0000.0000.0690.0591.0000.0300.0470.0450.0000.0150.0000.0000.0500.0460.0540.0380.0220.0480.0000.0000.0000.0430.1910.151
Soil_Type_310.0120.0000.0000.0740.0100.0320.1960.0540.0710.1320.0380.0000.0580.0660.0780.0570.0350.0860.0910.0560.0200.0150.0240.0100.0040.0280.0000.0000.0270.0070.0000.0180.0300.0150.0000.0000.0000.0000.0420.0360.0301.0000.0280.0270.0000.0040.0000.0000.0300.0280.0330.0220.0110.0290.0000.0000.0000.0000.1580.095
Soil_Type_320.0000.0130.0000.1110.0200.0490.2930.0820.1380.2350.0580.0000.0870.1080.0950.1100.0720.0860.1500.0850.0320.0260.0380.0200.0140.0430.0030.0060.0430.0160.0000.0300.0470.0260.0000.0040.0000.0000.0650.0560.0470.0281.0000.0430.0000.0140.0000.0000.0470.0430.0510.0360.0210.0450.0000.0000.0000.0340.2230.143
Soil_Type_330.0130.0000.0000.1070.0190.0470.2830.0790.0700.1590.0560.0000.0840.0840.0620.0460.0540.0710.1150.0820.0310.0250.0370.0190.0130.0420.0000.0050.0410.0160.0000.0290.0450.0250.0000.0030.0000.0000.0620.0540.0450.0270.0431.0000.0000.0130.0000.0000.0460.0420.0490.0350.0200.0430.0000.0000.0000.0100.2230.138
Soil_Type_340.0230.0000.0000.0130.0000.0000.0450.0070.0750.0470.0000.0000.0080.0320.0400.0310.0370.0310.0300.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.019
Soil_Type_350.0000.0230.0000.0420.0000.0160.0590.2120.0690.1530.0200.0000.0320.0380.0270.0360.0510.0700.0460.0310.0070.0000.0110.0000.0000.0130.0000.0000.0130.0000.0000.0050.0150.0000.0000.0000.0000.0000.0230.0190.0150.0040.0140.0130.0001.0000.0000.0000.0150.0130.0160.0090.0000.0140.0000.0000.0000.0080.0390.054
Soil_Type_360.0000.0090.0000.0100.0000.0000.0180.0750.0970.0730.0000.0000.0050.0300.0280.0560.0360.0550.0280.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.016
Soil_Type_370.0070.0110.0000.0210.0000.0000.0310.1160.0330.1140.0060.0000.0150.0250.0350.0210.0450.0910.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0050.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.028
Soil_Type_380.0130.0120.0000.1180.0220.0520.1650.5850.1630.4890.0620.0000.0930.1220.1010.1120.1180.2050.1740.0900.0340.0280.0410.0220.0150.0460.0050.0070.0460.0180.0000.0320.0500.0280.0000.0060.0000.0000.0690.0600.0500.0300.0470.0460.0000.0150.0000.0001.0000.0460.0540.0380.0220.0480.0000.0000.0000.0780.0800.152
Soil_Type_390.0000.0070.0000.1090.0190.0480.1520.5380.0780.4230.0570.0000.0850.0970.0630.0990.0580.1490.0860.0830.0310.0250.0370.0190.0130.0420.0010.0050.0420.0160.0000.0290.0460.0250.0000.0040.0000.0000.0630.0550.0460.0280.0430.0420.0000.0130.0000.0000.0461.0000.0490.0350.0200.0440.0000.0000.0000.0330.0970.140
Soil_Type_40.0230.0340.0000.1260.0230.0560.1760.0930.0570.2400.0660.0000.0990.0380.0520.1640.0660.1110.1080.0960.0370.0300.0440.0230.0170.0500.0070.0090.0490.0200.0000.0340.0540.0300.0000.0080.0000.0000.0740.0640.0540.0330.0510.0490.0000.0160.0000.0000.0540.0491.0000.0410.0240.0510.0000.0000.0000.0460.1730.040
Soil_Type_400.0190.0260.0000.0910.0150.0400.1280.4530.2330.6540.0470.0000.0720.0680.0760.0510.1640.1190.0500.0700.0260.0200.0310.0150.0090.0350.0000.0000.0350.0120.0000.0240.0380.0210.0000.0000.0000.0000.0530.0460.0380.0220.0360.0350.0000.0090.0000.0000.0380.0350.0411.0000.0160.0360.0000.0000.0000.2550.0110.117
Soil_Type_50.0110.0000.0000.0560.0040.0230.0790.0410.0310.2470.0280.0000.0440.0710.1050.1590.0860.1390.1120.0430.0130.0090.0170.0040.0000.0200.0000.0000.0200.0000.0000.0120.0220.0090.0000.0000.0000.0000.0320.0270.0220.0110.0210.0200.0000.0000.0000.0000.0220.0200.0240.0161.0000.0210.0000.0000.0000.0180.0920.164
Soil_Type_60.0180.0010.0000.1130.0200.0500.1580.0830.0960.3490.0590.0000.0890.0290.0280.0450.0910.1970.0420.0860.0330.0260.0390.0200.0140.0440.0030.0060.0440.0170.0000.0300.0480.0270.0000.0050.0000.0000.0660.0570.0480.0290.0450.0430.0000.0140.0000.0000.0480.0440.0510.0360.0211.0000.0000.0000.0000.0410.1830.323
Soil_Type_70.0000.0200.2880.0000.0000.0000.0000.0000.0000.0000.0000.2880.0010.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Soil_Type_80.0000.0100.6120.0000.0000.0000.0000.0000.0000.0170.0000.6120.0180.0000.0000.0000.0000.0110.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Soil_Type_90.0000.0180.0000.0250.0000.0000.0000.0000.0160.0380.0500.0000.0330.0000.0000.0000.0060.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0050.000
Wilderness_Area_20.0210.0220.0000.1010.0180.0420.0700.2020.0970.3900.1440.0000.0720.0600.0210.0740.0550.1030.0780.0780.0290.0230.0310.0180.0000.0400.0000.0250.0390.0150.0000.0870.1120.0830.0920.0000.0000.0000.0560.0510.0430.0000.0340.0100.0000.0080.0000.0000.0780.0330.0460.2550.0180.0410.0000.0000.0001.0000.1670.132
Wilderness_Area_30.0000.0000.0000.0290.0530.0260.0250.1280.1960.5260.0190.0000.0300.1710.1470.2130.1910.3330.1490.0950.1310.1110.2200.0530.0360.0000.0440.0000.0640.0340.0270.0510.0070.0760.0110.0650.0230.0210.2600.1690.1910.1580.2230.2230.0380.0390.0330.0020.0800.0970.1730.0110.0920.1830.0000.0000.0050.1671.0000.566
Wilderness_Area_40.0000.0050.0000.1840.1210.1240.4880.2600.2750.9230.1850.0000.0390.2220.2620.2130.4440.4680.3050.3390.0440.0880.1250.1210.0470.1260.0340.0380.0530.0620.0120.1000.1520.0890.0050.0360.0090.0080.2060.3390.1510.0950.1430.1380.0190.0540.0160.0280.1520.1400.0400.1170.1640.3230.0000.0000.0000.1320.5661.000

Missing values

2024-02-20T23:32:26.110974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-20T23:32:26.567805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationSlopeHillshade_9amHillshade_NoonHillshade_3pmAspect_sinAspect_cosHorizontal_Distance_To_Roadways_logHorizontal_Distance_To_Fire_Points_logDistance_To_Hydrology_logWilderness_Area_2Wilderness_Area_3Wilderness_Area_4Climatic_Zone_3Climatic_Zone_4Climatic_Zone_5Climatic_Zone_6Climatic_Zone_7Climatic_Zone_8Geologic_Zone_2Geologic_Zone_5Geologic_Zone_7Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_40
0-1.092254-0.5339320.8910890.7548390.454183-0.8979280.440143-0.106266-0.570841-0.6612570.00.01.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1-0.7533490.1691120.9455450.8645160.442231-0.587795-0.809010-0.4331860.0273440.0855890.01.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
2-1.1208941.4580250.6287130.9870970.7649400.3631990.9317110.342986-0.206036-0.2976550.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3-1.185334-1.2369760.8465350.9290320.613546-0.8011350.598484-1.376445-1.300324-2.6390440.00.01.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
4-1.521853-1.2369760.8811880.8709680.545817-0.864551-0.502544-0.0592810.139235-2.6390440.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
50.986526-0.8854540.9108910.8709680.509960-0.6160400.7877150.5520360.573177-2.6390440.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
6-0.037351-0.0652360.7970300.6709680.482072-0.9880320.154251-0.302236-0.364589-0.7003820.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
71.377938-1.3541500.8168320.8322580.589641-0.846220-0.5328330.560376-0.174283-0.3306030.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.0
81.513977-0.4167580.9306930.7806450.430279-0.9482820.317429-0.605955-1.8521971.1055431.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0
9-1.0564552.0438950.3811880.9161290.936255-0.727163-0.6864650.192362-0.657367-0.0613470.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
ElevationSlopeHillshade_9amHillshade_NoonHillshade_3pmAspect_sinAspect_cosHorizontal_Distance_To_Roadways_logHorizontal_Distance_To_Fire_Points_logDistance_To_Hydrology_logWilderness_Area_2Wilderness_Area_3Wilderness_Area_4Climatic_Zone_3Climatic_Zone_4Climatic_Zone_5Climatic_Zone_6Climatic_Zone_7Climatic_Zone_8Geologic_Zone_2Geologic_Zone_5Geologic_Zone_7Soil_Type_2Soil_Type_3Soil_Type_4Soil_Type_5Soil_Type_6Soil_Type_7Soil_Type_8Soil_Type_9Soil_Type_10Soil_Type_11Soil_Type_12Soil_Type_13Soil_Type_14Soil_Type_16Soil_Type_17Soil_Type_18Soil_Type_19Soil_Type_20Soil_Type_21Soil_Type_22Soil_Type_23Soil_Type_24Soil_Type_25Soil_Type_26Soil_Type_27Soil_Type_28Soil_Type_29Soil_Type_30Soil_Type_31Soil_Type_32Soil_Type_33Soil_Type_34Soil_Type_35Soil_Type_36Soil_Type_37Soil_Type_38Soil_Type_39Soil_Type_40
151101.296791-0.4167580.7673270.7419350.5657370.4201670.9074470.7878760.8223941.0587180.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.0
15111-0.755735-0.6511060.9207920.8709680.494024-0.930106-0.367291-0.715315-0.679475-2.6390440.01.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
151122.062909-1.2369760.8861390.8580650.537849-0.7271430.6864870.4499000.5025200.4757251.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.0
15113-0.4311501.6923730.5297030.9741940.8486060.329962-0.9439940.592054-1.9582130.6597110.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
151141.3636180.1691120.6782180.6838710.605578-0.841487-0.5402771.3267821.4953610.7435150.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.0
151150.829006-0.5339320.8712870.7419350.466135-0.5215510.8532201.3270631.1059840.1724570.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0
15116-1.445480-1.2369760.8811880.8580650.5378490.4520260.892005-0.320211-0.692550-2.6390440.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
15117-0.108951-1.1198020.8811880.8322580.5258960.3132290.9496780.6390000.985553-2.6390440.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
151181.036646-1.4713240.8316830.8451610.581673-0.158623-0.9873390.540820-0.2815060.7228810.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.0
15119-1.4621870.9893290.4405940.7677420.8486060.9866330.162961-0.877202-1.7245980.0756900.00.01.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0